To better inform the next clinical trials of vascular disrupting agent combretastatin-A4-phosphate (CA4P) in patients with hepatic malignancies, this preclinical study aimed at evaluating CA4P therapeutic efficacy in rats with primary hepatocellular carcinomas (HCCs) of a full spectrum of differentiation and vascularity by magnetic resonance imaging (MRI), microangiography and histopathology. Ninety-six HCCs were raised in 25 rats by diethylnitrosamine gavage. Tumor growth was monitored by T2-/T1-weighted-MRI (T2WI, T1WI) using a 3.0 T scanner. Early vascular response and later intratumoral necrosis were detected by dynamic-contrast-enhanced (DCE) MRI and diffusion-weighted-imaging (DWI) before, 1 and 12 hr after CA4P iv-administration. In vivo MRI-findings were validated by postmortem-techniques. Multi-parametric MRI revealed rapid CA4P-induced tumor vascular shutdown within 1 hr, followed by variable intratumoral necrosis at 12 hr. Tumor volumes decreased by 10% at 1 hr (p < 0.05), but resumed at 12 hr. Correlations of semi-quantitative DCE parameter initial-area-under-the-gadolinium-curve (IAUGC30) with histopathology proved partial vascular closure and compensational reopening (p < 0.05). The higher grades of vascularity prevented those residual tumor tissues from CA4P-caused ischemic necrosis. By histopathology using a 4-scale cellular-differentiation criteria and a 4-grade tumor-vascularity classification, percentage of CA4P-induced necrosis negatively correlated with HCC differentiation (r = -0.404, p < 0.001) and tumor vascularity (r = -0.370, p < 0.001). Ordinal-logistic-regression helped to predict early tumor responses to CA4P in terms of tumoral differentiation and vascularity. Our study demonstrated that CA4P could induce vascular shutdown in primary HCCs within 1 hr, resulting in various degrees of tumor necrosis at 12 hr. MRI as a real-time imaging biomarker may help to define tumor vascularity and differentiation and further to predict CA4P therapeutic outcomes.
The field of Text-to-Speech has experienced huge improvements last years benefiting from deep learning techniques. Producing realistic speech becomes possible now. As a consequence, the research on the control of the expressiveness, allowing to generate speech in different styles or manners, has attracted increasing attention lately. Systems able to control style have been developed and show impressive results. However the control parameters often consist of latent variables and remain complex to interpret.In this paper, we analyze and compare different latent spaces and obtain an interpretation of their influence on expressive speech. This will enable the possibility to build controllable speech synthesis systems with an understandable behaviour.
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